Logistic Regression Classification Learner
Source:R/LearnerClassifLogReg.R
mlr_learners_classif.log_reg.Rd
Classification via logistic regression.
Calls stats::glm()
with family
set to "binomial"
.
Internal Encoding
Starting with mlr3 v0.5.0, the order of class labels is reversed prior to
model fitting to comply to the stats::glm()
convention that the negative class is provided
as the first factor level.
Initial parameter values
model
:Actual default:
TRUE
.Adjusted default:
FALSE
.Reason for change: Save some memory.
Offset
If a Task
has a column with the role offset
, it will automatically be used during training.
The offset is incorporated through the formula interface to ensure compatibility with stats::glm()
.
We add it to the model formula as offset(<column_name>)
and also include it in the training data.
During prediction, the default behavior is to use the offset column from the test set (enabled by use_pred_offset = TRUE
).
Otherwise, if the user sets use_pred_offset = FALSE
, a zero offset is applied, effectively disabling the offset adjustment during prediction.
Dictionary
This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn()
:
Meta Information
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3learners, 'stats'
Parameters
Id | Type | Default | Levels | Range |
dispersion | untyped | NULL | - | |
epsilon | numeric | 1e-08 | \((-\infty, \infty)\) | |
etastart | untyped | - | - | |
maxit | numeric | 25 | \((-\infty, \infty)\) | |
model | logical | TRUE | TRUE, FALSE | - |
mustart | untyped | - | - | |
singular.ok | logical | TRUE | TRUE, FALSE | - |
start | untyped | NULL | - | |
trace | logical | FALSE | TRUE, FALSE | - |
x | logical | FALSE | TRUE, FALSE | - |
y | logical | TRUE | TRUE, FALSE | - |
use_pred_offset | logical | TRUE | TRUE, FALSE | - |
Contrasts
To ensure reproducibility, this learner always uses the default contrasts:
contr.treatment()
for unordered factors, andcontr.poly()
for ordered factors.
Setting the option "contrasts"
does not have any effect.
Instead, set the respective hyperparameter or use mlr3pipelines to create dummy features.
See also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners
Package mlr3extralearners for more learners.
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).mlr3pipelines to combine learners with pre- and postprocessing steps.
Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Other Learner:
mlr_learners_classif.cv_glmnet
,
mlr_learners_classif.glmnet
,
mlr_learners_classif.kknn
,
mlr_learners_classif.lda
,
mlr_learners_classif.multinom
,
mlr_learners_classif.naive_bayes
,
mlr_learners_classif.nnet
,
mlr_learners_classif.qda
,
mlr_learners_classif.ranger
,
mlr_learners_classif.svm
,
mlr_learners_classif.xgboost
,
mlr_learners_regr.cv_glmnet
,
mlr_learners_regr.glmnet
,
mlr_learners_regr.kknn
,
mlr_learners_regr.km
,
mlr_learners_regr.lm
,
mlr_learners_regr.nnet
,
mlr_learners_regr.ranger
,
mlr_learners_regr.svm
,
mlr_learners_regr.xgboost
Super classes
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifLogReg
Examples
if (requireNamespace("stats", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.log_reg")
print(learner)
# Define a Task
task = tsk("sonar")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
# print the model
print(learner$model)
# importance method
if("importance" %in% learner$properties) print(learner$importance)
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
}
#>
#> ── <LearnerClassifLogReg> (classif.log_reg): Logistic Regression ───────────────
#> • Model: -
#> • Parameters: use_pred_offset=TRUE
#> • Packages: mlr3, mlr3learners, and stats
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, character, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: offset, twoclass, and weights
#> • Other settings: use_weights = 'use'
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#>
#> Call: stats::glm(formula = form, family = "binomial", data = data,
#> model = FALSE)
#>
#> Coefficients:
#> (Intercept) V1 V10 V11 V12 V13
#> -285.756 -677.894 -138.149 -256.903 518.509 -95.833
#> V14 V15 V16 V17 V18 V19
#> 5.969 88.531 -317.354 127.805 73.806 -146.011
#> V2 V20 V21 V22 V23 V24
#> 1042.352 258.374 -233.810 407.294 -305.507 388.313
#> V25 V26 V27 V28 V29 V3
#> -295.362 -67.455 220.419 -15.845 -171.077 -592.316
#> V30 V31 V32 V33 V34 V35
#> 366.534 -363.831 176.149 -62.702 89.860 53.869
#> V36 V37 V38 V39 V4 V40
#> -126.325 -105.908 -4.911 504.074 146.961 -543.467
#> V41 V42 V43 V44 V45 V46
#> 307.564 -245.771 50.703 528.297 -947.786 853.212
#> V47 V48 V49 V5 V50 V51
#> -368.984 1132.261 582.159 80.766 -4700.025 2208.161
#> V52 V53 V54 V55 V56 V57
#> 682.343 1965.509 -1969.410 -5174.850 -1851.471 -2349.680
#> V58 V59 V6 V60 V7 V8
#> 6718.001 2307.788 135.176 2462.877 4.320 123.704
#> V9
#> 175.307
#>
#> Degrees of Freedom: 138 Total (i.e. Null); 78 Residual
#> Null Deviance: 192.1
#> Residual Deviance: 8.973e-09 AIC: 122
#> classif.ce
#> 0.3043478